{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cd1d69a2",
   "metadata": {},
   "source": [
    "# 绘制 x 和 y 点\n",
    "plot() 函数用于在图表中绘制点（标记）。\n",
    "\n",
    "默认情况下，plot() 函数从点到点绘制一条线。\n",
    "\n",
    "该函数采用参数来指定图中的点。\n",
    "\n",
    "参数 1 是一个包含 x 轴上的点的数组。\n",
    "\n",
    "参数 2 是一个包含 y 轴上的点的数组。\n",
    "\n",
    "如果我们需要绘制一条从 (1, 3) 到 (8, 10) 的线，我们必须将两个数组 [1, 8] 和 [3, 10] 传递给 plot 函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a9442b7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c2315dee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JSiFzbzEXDu/O3RcOpWNrDZ8Sd6nARY6jrLKGRz7YxLOfZhPVpgXPXJvAOUO7uB1LBFCBixzTmq0HmJecQs6BUq4cG828qYNp21LDpyRwqMBFjlBUXsXCdzP559od9O4Yzj9vHseEfho+JYFHBS7yPR9l7uO25DTyisu5+bQ+/P6cQbQK0zZ4CUwqcBHgwOEK7l2WwdLvdjOoSxuevmY0I3q1czuWyHGpwCWkWWt5a8Nu7nk7g+LyKn579gB+fkZ/wpppG7wEPhW4hKw9hWXMX5LGh5l5DO/VjkUz4hnUtY3bsUROmgpcQo7PZ/nX1zt58J2NVPl8zJ82hBsm9qGptsGLx6jAJaTk7C9hbnIKX2YXML5vRxbOiKN3Rw2fEm9SgUtIqK7x8cLnOfz5/SyaN2nCwkvj+PGYXtoGL56mApegl7m3iDmJKWzYVcjZQzpz3yVxdI1s6XYsEb+pwCVoVVTX8MSqrTy5aguRrZrztytHckF8N626JWiowCUofbvjIHOSUti07zDTR/bgjguG0iEizO1YIo5SgUtQKa2s5s8rN/H859vo2rYlz1+fwOTBGj4lwUkFLkHjiy37mZucyo6CUq4+JZo5UwbTRsOnJIipwMXzCsuqePCdjfzr653EdAznX7NO4ZS+ulGUBD8VuHjayvS9zH8zjf2HK7jl9L787uyBtGyu4VMSGvy9qXE74FkgFrDATGvtGgdyiRzX/sMV3P1WOstS9jC4axuevS6B+J7t3I4l0qj8XYE/Bqyw1l5mjAkDwh3IJHJM1lre/C6Xe97OoLSihj+cM5CfntGP5k01fEpCT70L3BgTCUwCrgew1lYClc7EEvlfuw+VcfuSVFZl5TMyunb41IAuGj4locufFXgfIB94wRgzHFgP/MZaW/L9JxljZgGzAKKjo/04nYQqn8/y6lc7eOjdTGp8ljsvGMp1E2I0fEpCnj9fdzYDRgFPWWtHAiXA3COfZK1dbK1NsNYmREVF+XE6CUXZ+Ye5YvGX3PFmGiN6tWPl7yYx81RNDhQB/1bgu4Bd1tq1dT9O5CgFLlIf1TU+nv1sG4+8v4kWzZqw6LJ4Lh/dU9vgRb6n3gVurd1rjNlpjBlkrc0CzgIynIsmoSpjdxGzkzaQllvEecO6sODiWDq31fApkSP5+y6UXwGv1r0DJRu4wf9IEqoqqmt4/KMtPPXxVtqFN+fJq0ZxfmxXrbpFjsGvArfWfgckOBNFQtn67bXDp7bkHebSUT24Y9pQ2mv4lMhxaSemuKqkopqHV2bx4hc5dI9sxYs3jOGMQZ3djiXiCSpwcc2nm/OZl5zKroNlXDu+N7OnDKZ1C/2TFDlZ+myRRldYWsV9yzN4Y/0u+naK4PVbxjO2Twe3Y4l4jgpcGtWKtL3csTSNgpJKfn5GP3591gANnxKpJxW4NIq84nLufiudd1L3MrRbW164fgyxPSLdjiXiaSpwaVDWWpK+yWXBsgzKqmq49bxBzJrUV8OnRBygApcGs+tgKbctSWP1pnxG927PQzPi6d+5tduxRIKGClwc5/NZ/vHldh5akQnAPRcN45pTetNE80tEHKUCF0dtzT/MnMQU1m0/yKSBUTwwPZae7TUmXqQhqMDFEVU1PhavzuaxDzfTqnlTHr58ODNG9dA2eJEGpAIXv6XlFjInKYX03UVMjevK3RcNo3MbDZ8SaWgqcKm38qoa/vrhZv6+Opv24WE8ffUopsR2czuWSMhQgUu9fJ1TwJzEFLL3l3D56J7MnzaUyPDmbscSCSkqcPlBDldUs2hFJi+v2U6Pdq14eeZYJg3UnZZE3KACl5P2yaZ8bktOZXdhGddPiOHW8wYRoeFTIq7RZ5+c0KHSSu5dlkHyN7n0i4og8afjGd1bw6dE3KYCl+N6J3UPdy5N41BpFb88sz+/nNxfw6dEAoQKXI4qr6icO5am8V76PmJ7tOWlmWMZ1l3Dp0QCiQpc/ou1ljfW7+K+ZRmUV/uYM2UwN5/Wh2YaPiUScPwqcGNMDlAM1ADV1lrdH9PDdhaUMi85lc+27GdsTAcWzoijb5SGT4kEKidW4Gdaa/c7cBxxSY3P8vKaHBatyKKJgQWXxHLV2GgNnxIJcLqEEuK25BUzOzGFb3Yc4oxBUdw/PY4e7Vq5HUtEToK/BW6BlcYYC/zdWrv4yCcYY2YBswCio6P9PJ04parGx9Mfb+VvH20hvEVTHvnxcC4ZoeFTIl7ib4Gfaq3NNcZ0Bt43xmRaa1d//wl1pb4YICEhwfp5PnFA6q5Cbk3cQObeYqbFd+Oei4bRqXULt2OJyA/kV4Fba3Prvs8zxiwBxgKrj/+rxC3lVTU88sEmnlmdTafWLfj7NaM5b1hXt2OJSD3Vu8CNMRFAE2ttcd3H5wL3OpZMHLU2+wBzk1PZtr+EK8b0Yt7UIUS20vApES/zZwXeBVhSd820GfBPa+0KR1KJY4rLq3hoRSavfLmDXh1a8epN45jYv5PbsUTEAfUucGttNjDcwSzisFWZedy+JJU9ReXceGof/nDuQMLD9MYjkWChz+YgVFBSyYJlGSz5NpcBnVuT9LMJjIpu73YsEXGYCjyIWGtZlrKHu99Kp7Csil+fNYBfnNmPFs00fEokGKnAg8S+onJuX5LGBxv3Ed8zklduGseQbm3djiUiDUgF7nHWWv799U7uf2cjldU+bps6mJkTNXxKJBSowD1sx4FS5ian8MXWA4zr04GHZsQT0ynC7Vgi0khU4B5U47O88Pk2Hl6ZRbMmTXhgehxXjOml4VMiIUYF7jFZe4uZnZTChp2HmDy4M/dPj6VbpIZPiYQiFbhHVFb7ePLjLTyxagttWjbnsStGcNHw7ho+JRLCVOAesGHnIWYnppC1r5iLhnfnrguH0lHDp0RCngo8gJVV1vCX97N47rNtdG7TkmevTeDsoV3cjiUiAUIFHqDWbD3A3OQUth8o5Sfjopl7/mDattTwKRH5PyrwAFNUXsWD72Ty2lc76N0xnH/ePI4J/TR8SkT+lwo8gHyQsY/b30wlv7iCWZP68ruzB9IqTNvgReToVOAB4MDhCu55O4O3NuxmUJc2/P2aBEb0aud2LBEJcCpwF1lreWvDbu5+K53DFdX87uyB/OyMfoQ10zZ4ETkxFbhL9hSWMX9JGh9m5jGiVzsWXRbPwC5t3I4lIh6iAm9kPp/lta938OA7mVT7fMyfNoQbJvahqbbBi8gPpAJvRNv2lzA3KYW12wqY0K8jCy+NJ7pjuNuxRMSjVOCNoLrGx/Ofb+PPKzcR1rQJCy+N48djemkbvIj4RQXewDbuKWJOUgopuwo5e0gX7rsklq6RLd2OJSJBwO8CN8Y0BdYBudbaC/yPFBwqqmt4YtVWnly1hchWzXn8JyOZFtdNq24RcYwTK/DfABsB3b+rzjc7DjInMYXNeYeZPrIHd14wlPYRYW7HEpEg41eBG2N6AtOA+4HfO5LIw0orq/nzyk08//k2urZtyQvXj+HMwZ3djiUiQcrfFfijwGzgmG9gNsbMAmYBREdH+3m6wPX5lv3MTU5hZ0EZV58SzZwpg2mj4VMi0oDqXeDGmAuAPGvtemPMGcd6nrV2MbAYICEhwdb3fIGqsKyKB5Zv5N/rdtKnUwT/nnUK4/p2dDuWiIQAf1bgE4GLjDFTgZZAW2PMK9baq52JFvhWpu9l/ptp7D9cwS2n1w6fatlcw6dEpHHUu8CttfOAeQB1K/A/hkp55xdXcPfb6SxP2cPgrm149roE4nu2czuWiIQYvQ/8B7DW8uZ3udzzdgalFTX88dyB3HJ6P5o31fApEWl8jhS4tfZj4GMnjhWocg+VcfuSVD7OymdUdO3wqf6dNXxKRNyjFfgJ+HyWV9duZ+G7mfgs3HXhUK4dH6PhUyLiOhX4cWTnH2ZuUipf5RRwav9OPHhpHL06aPiUiAQGFfhRVNf4eObTbTzywSZaNmvCosviuXx0T22DF5GAogI/QsbuImYnbSAtt4jzhnVhwcWxdG6r4VMiEnhU4HXKq2p4/KMtPP3JVtqFh/HUVaM4P66b27FERI5JBQ6s317A7MQUtuaXMGNUT+64YAjtwjV8SkQCW0gXeElFNX96L4uX1uTQPbIVL80cy+kDo9yOJSJyUkK2wFdvymdeciq5h8q4bnxvbp0ymNYtQvaPQ0Q8KOQaq7C0igXLM0hcv4u+URG88dPxjInp4HYsEZEfLKQKfEXaHu5Ymk5BSSU/P6Mfvz5rgIZPiYhnhUSB5xWXc9fSdN5N28vQbm154foxxPaIdDuWiIhfgrrArbUkrt/Ffcs3UlZVw63nDWLWpL4aPiUiQSFoC3xnQSm3LUnl0837SejdnoUz4unfubXbsUREHBN0Be7zWV5ek8Oi97IAuOeiYVxzSm+aaPiUiASZoCrwLXmHmZuUwrrtB5k0MIoHpsfSs72GT4lIcAqKAq+q8bF4dTaPfbCZVmFN+fPlw7l0VA8NnxKRoOb5Ak/LLWR2YgoZe4qYGteVey6KJapNC7djiYg0OM8WeHlVDY99uJnFq7PpEBHG01ePYkqshk+JSOjwZIF/nVPAnMQUsveXcPnonsyfNpTI8OZuxxIRaVT1LnBjTEtgNdCi7jiJ1tq7nAp2NIcrqlm0IpOX12ynZ/tW/OPGsZw2QMOnRCQ0+bMCrwAmW2sPG2OaA58ZY9611n7pULb/8nFWHrcvSWN3YRk3TIzhj+cOIkLDp0QkhNW7Aa21Fjhc98Pmdd+sE6GONC85lde+2kH/zq1J/OkERvdu3xCnERHxFL+WsMaYpsB6oD/whLV27VGeMwuYBRAdHV2v88R0DOdXk/vzy8n9adFMw6dERABM7ULaz4MY0w5YAvzKWpt2rOclJCTYdevW+X0+EZFQYoxZb61NOPJxR6Y6WWsPAauAKU4cT0RETqzeBW6MiapbeWOMaQWcA2Q6lEtERE7An2vg3YCX6q6DNwFet9YucyaWiIiciD/vQkkBRjqYRUREfgDd2UBExKNU4CIiHqUCFxHxKBW4iIhHObKR56RPZkw+sL2ev7wTsN/BOA3NS3mVteF4Ka+XsoK38vqbtbe19n8m9zVqgfvDGLPuaDuRApWX8iprw/FSXi9lBW/lbaisuoQiIuJRKnAREY/yUoEvdjvAD+SlvMracLyU10tZwVt5GySrZ66Bi4jIf/PSClxERL5HBS4i4lEBX+DGmOeNMXnGmGPeKCJQGGN6GWNWGWMyjDHpxpjfuJ3peIwxLY0xXxljNtTlvcftTCdijGlqjPnWGBPwky+NMTnGmFRjzHfGmIC+k4kxpp0xJtEYk2mM2WiMGe92pmMxxgyq+zP9z7ciY8xv3c51LMaY39V9fqUZY16ruyG8M8cO9GvgxphJ1N5782VrbazbeY7HGNMN6Gat/cYY04ba281dYq3NcDnaURljDBDx/RtTA79pqBtTO8EY83sgAWhrrb3A7TzHY4zJARKstQG/2cQY8xLwqbX2WWNMGBBed6OWgFY3zjoXGGetre8mwQZjjOlB7efVUGttmTHmdeAda+2LThw/4Ffg1trVQIHbOU6GtXaPtfabuo+LgY1AD3dTHZut1Sg3pnaCMaYnMA141u0swcQYEwlMAp4DsNZWeqG865wFbA3E8v6eZkArY0wzIBzY7dSBA77AvcoYE0PtvPT/udFzIKm7JPEdkAe8f7QbUweQR4HZgM/lHCfLAiuNMevrbu4dqPoA+cALdZennjXGRLgd6iRdAbzmdohjsdbmAg8DO4A9QKG1dqVTx1eBNwBjTGsgCfittbbI7TzHY62tsdaOAHoCY40xAXmZyhhzAZBnrV3vdpYf4FRr7SjgfOAXdZcDA1EzYBTwlLV2JFACzHU30onVXeq5CHjD7SzHYoxpD1xM7X+S3YEIY8zVTh1fBe6wumvJScCr1tpkt/OcLA/cmHoicFHddeV/AZONMa+4G+n46lZfWGvzgCXAWHcTHdMuYNf3vvpKpLbQA935wDfW2n1uBzmOs4Ft1tp8a20VkAxMcOrgKnAH1b0o+Byw0Vr7F7fznIiXbkxtrZ1nre1prY2h9svmj6y1jq1knGaMiah7IZu6yxHnAgH5Tipr7V5gpzFmUN1DZwEB+cL7Ea4kgC+f1NkBnGKMCa/rh7OofW3MEQFf4MaY14A1wCBjzC5jzI1uZzqOicA11K4O//MWp6luhzqObsAqY0wK8DW118AD/u15HtEF+MwYswH4ClhurV3hcqbj+RXwat2/hRHAA+7GOb66/xTPoXZFG7DqvqpJBL4BUqntXMe21Qf82whFROToAn4FLiIiR6cCFxHxKBW4iIhHqcBFRDxKBS4i4lEqcBERj1KBi4h41P8D5ilyG1evuj4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 在图中从位置 (1, 3) 到位置 (8, 10) 画一条线：\n",
    "xpoints = np.array([1, 8])\n",
    "ypoints = np.array([3, 10])\n",
    "\n",
    "plt.plot(xpoints, ypoints)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e4a185e",
   "metadata": {},
   "source": [
    "# 无线绘图\n",
    "仅绘制标记，您可以使用快捷字符串符号参数“o”，这意味着“环”。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4b76ce6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 在图中绘制两个点，一个在位置 (1, 3)，一个在位置 (8, 10)：\n",
    "xpoints = np.array([1, 8])\n",
    "ypoints = np.array([3, 10])\n",
    "\n",
    "plt.plot(xpoints, ypoints, 'o')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e66cab71",
   "metadata": {},
   "source": [
    "# 多点\n",
    "您可以根据需要绘制任意数量的点，只需确保两个轴上的点数相同即可。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "73490dc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 在图中画一条线，从位置 (1, 3) 到 (2, 8) 然后到 (6, 1) 最后到位置 (8, 10)：\n",
    "xpoints = np.array([1, 2, 6, 8])\n",
    "ypoints = np.array([3, 8, 1, 10])\n",
    "\n",
    "plt.plot(xpoints, ypoints)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91344e48",
   "metadata": {},
   "source": [
    "# 默认 X 点\n",
    "如果我们不指定 x 轴上的点，它们将获得默认值 0、1、2、3（等等，取决于 y 点的长度。\n",
    "\n",
    "因此，如果我们采用与上述相同的示例，并省略 x 点，则图表将如下所示："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9dd20c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ypoints = np.array([3, 8, 1, 10, 5, 7])\n",
    "\n",
    "plt.plot(ypoints)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
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